text: dataset_type = 'CocoDataset'
  img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[
                      58.395, 57.12, 57.375], to_rgb=True)
  train_pipeline = [
      dict(type='LoadImageFromFile', to_float32=True),
      dict(type='LoadAnnotations', with_bbox=True),
      dict(
          type='PhotoMetricDistortion',
          brightness_delta=32,
          contrast_range=(0.5, 1.5),
          saturation_range=(0.5, 1.5),
          hue_delta=18),
      dict(
          type='MinIoURandomCrop',
          min_ious=(0.1, 0.3, 0.5, 0.7, 0.9),
          min_crop_size=0.1),
      dict(
          type='Resize',
          img_scale=[(1344, 480), (1344, 960)],
          multiscale_mode='range',
          keep_ratio=False),
      dict(type='RandomFlip', flip_ratio=0.5),
      dict(
          type='Normalize',
          mean=[123.675, 116.28, 103.53],
          std=[58.395, 57.12, 57.375],
          to_rgb=True),
      dict(type='Pad', size_divisor=32),
      dict(type='DefaultFormatBundle'),
      dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
  ]
  test_pipeline = [
      dict(type='LoadImageFromFile'),
      dict(
          type='MultiScaleFlipAug',
          img_scale=(1344, 800),
          flip=False,
          transforms=[
              dict(type='Resize', keep_ratio=False),
              dict(
                  type='Normalize',
                  mean=[123.675, 116.28, 103.53],
                  std=[58.395, 57.12, 57.375],
                  to_rgb=True),
              dict(type='Pad', size_divisor=32),
              dict(type='ImageToTensor', keys=['img']),
              dict(type='Collect', keys=['img'])
          ])
  ]
  data = dict(
      samples_per_gpu=4,
      workers_per_gpu=4,
      train=dict(
          type='RepeatDataset',
          times=1,
          adaptive_repeat_times=True,
          dataset=dict(
              type=dataset_type,
              ann_file='data/coco/annotations/instances_train2017.json',
              img_prefix='data/coco/train2017',
              pipeline=train_pipeline)),
      val=dict(
          type=dataset_type,
          ann_file='data/coco/annotations/instances_val2017.json',
          img_prefix='data/coco/val2017',
          test_mode=True,
          pipeline=test_pipeline),
      test=dict(
          type=dataset_type,
          ann_file='data/coco/annotations/instances_val2017.json',
          img_prefix='data/coco/val2017',
          test_mode=True,
          pipeline=test_pipeline))
  